Machine Learning and Data Science

Machine Learning Training in Jaipur

MACHINE LEARNING AND DATA SCIENCE

The world has changed and setting up new process and method of doing their even more precisely we are changing our self into a machine and dependencies is increasing more than our imagination.

 

Overview

The Main purpose of training machine is to use their speed and capability. Most importantly machine can think and perform task like humans.

Goals

  • Python Programming for ML
  • Supervised  based algo implementation
  • Tensor Flow and other frameworks learning
  • Learning pandas framework to handle data frames for machines
  • Matplotlib for graph plots with linear regression
  • Image recognition

Our Training Program Details

Machine Learning with Data Science 30 Days
Course:Machine Learning
Certification By:TechieNest, An ISO 9001:2008 Certified Company
Study Material:Book free to each participant (Soft Copy)
FeeINR 9,500/- + Taxes
Duration1 Months/ 50 Hours

Course Details


Machine Learning
DAYTOPICDURATION
Day 1

Basics of Python Programming

  • Environment Setup
  1. Installing Python
  2. Setting up path
  • Basic Syntax
  • Data Types
  1. Numbers
  2. String
  3. List
  4. Tuple
  5. Dictionary
  • Decision Making(Loops, Conditional Statements)
  • Functions
  • Basic Libraries
  1. Math
  2. Date & Time
  3. Random
  4. Request
4 Hours
Day 2

Machine Learning

  • Introduction to machine learning
  • Understanding the need
  • Understanding Big data and machine learning
  • Running machine learning under Linux platform
  • Why Linux is important for machine learning with respect to future
  • Basic Introduction of Python syntax and programming logic
  • Deep dive with Supervised, Unsupervised and Reinforcement learning
  • Algo discussion with use case

Advance Python programming and its use case

  • Basic of python and why python for machine learning
  • Installation of software and libraries on different OS.
  • Revising python concepts
  • Advance python programming
  • Hands-on with Python standard libraries
  • GITHUB exposure
4 Hours
Day 3Data Science Libraries

  • Understanding & use of Various Open source libraries
  • Importing various modules with different methods
  • File handling with Python
  • Working with Numpy
  • Data types and its various Numerical operations
  • Exploring various use cases of Numpy
  • Hands-on with huge data using Numpy
4 Hours
Day 4

Pandas & Matplotlib Libraries

  • Fundamentals of pandas
  • Data frames and their operations
  • .csv .xml and various files data import
  • Data extraction, update and export
  • Fundamentals of Matplotlib
  • Various 2D & 3D graphs
  • Data visualisation in various types of graphs

Project – Data Analytics using Python

4 Hours
Day 5

Computer Vision & OpenCV Library

  • Fundamentals of Computer Vision.
  • Image Processing using Python.
  • OpenCV library for various data operations.
  • Working with live data.
  • Computer Vision for various fields like AR, VR, ML etc.
  • Morphological operations and Image Filtering & ROI Extractions.
  • Color Marker Detection.
  • Face Detection
4 Hours
Day 7

Naive Bayes

  • Probability of Various Events
  • Bayes Theorem
  • Practice lab with Decision Tree algo and number of examples
  • Training data with python using Naive Bayes/li>
  • Deep dive with UCI
  • Lab session for loading data from different apis
  • Detecting data from numpy and converting for training and testing data
  • Exercise with ML and others framework
2 Hours
Day 8

ML Continued with Real Data set

  • Introduction to iris datasets
  • Understanding iris datasets
  • Modifying and loading with pandas
  • Separating data with numpy
  • Training classifier
  • Algo data process view
  • Decision Tree & Naive Bayes understanding & Results Comparisons
2 Hours
Day 9

K Nearest Neighbours – KNN algo)

  • Understanding the Mathematics and working of KNN
  • Implementing KNN by your Own
  • Apply your own designed KNN on real datasets
  • Comparing Designed KNN Results with Sklearn implementations
  • Applications of KNN
2 Hours
Day 10

Regression (Linear Regression)

  • Understanding functioning of the Algo and Its Mathematics
  • Implementing algo and applying datasets to it
  • Difference between Regression and Classification
  • Working with the real datasets
  • Stock exchange/GDP/Growth of the company analysis
  • Writing various codes upon various datasets
2 Hours
Day 11

SVM (Supprt Vector Machine)

  • Support Vector Classifier and Regression
  • Understanding functioning of the Algo and Its Mathematics
  • Understanding Hyperplanes and its various internal parameters
  • Implementing algo and applying datasets to it
  • Difference between Regression and Classification
  • Working with the real datasets
  • Writing various codes upon various datasets
2 Hours
Day 12

Clustering (K-Means)

  • Unsupervised Learning
  • Features and data vectors visualisation
  • Various steps of algo implementation
  • Understanding of Clusters and various types of Clustering
  • Applying K-Means on datasets and their practical usecases
  • Applications of Clustering and the algorithm
2 Hours
Day 13

Neural Network (NN)

  • What’s Neural Network?
  • Various Structures of NN
  • Understanding Fundamentals and Various parameters of NN
  • ANN,CNN and RNN
  • Deep Dive with the Implementaion of NN on various datasets
  • Applying CNN on Images
  • Applications and its complexities over other algorithms

Project 2:- Smart Machine Learning System

2 Hours
Day 14

Objects Detections

  • Image processing and it’s various features for detection
  • Haar Classifier and its fuctioning behind
  • Cascading of features in Algorithm
  • Implementation of Haar Classifier on different image datasets
  • Realtime Object Detection

Project 3:- Object Detection System

2 Hours
Day 15

Tensorflow

  • Fundamentals of tensorflow
  • What’s tensor and its flow graphs
  • Datatypes and Data Optimizers
  • Understanding Tensorflow from basics
  • Implementing usecase using tensorflow
  • Working on Realtime problem with Tensorflow and writing code for that
2 Hours
Day 16

Obejcts Recognitions

  • Understanding of Features of Objects for Recognitions
  • Working with Face Recognition Library
  • Recognition encodings
  • Various matching techniques for Recognition
  • Working on improving Efficiency of the code

Face Recognition System

Biometric Advance Attendance System

2 Hours
Day 17

Projects Continued

Project 6:- Smart Music App using ML & Python

Project 7:- Building Security System

2 Hours
Day 18

API Integration with Python

  • What is an API?
  • What is Cloud & its Connections with Python?
  • Google Python Libraries
  • Speech Recognition
  • Text to Speech Conversion
  • Speech Recognition Exceptions
  • Various API’s Integration for ML

Project 8 :- Design & Development of your Personal Assistant

2 Hours
Day 19

ML over Cloud

  • Various cloud platforms for ML
  • Open Source Cloud for Features engineering
  • Various features for a person analysis
  • Registration and deletion of data over cloud
  • Recognising images over cloud

Project 9:- Gender & Expressions recognition system over cloud

2 Hours
Day 20

Weather & Other API’s

  • Various weather API’s
  • Data extration from the raw weather information
  • Other API’s for data extraction from Web
  • Web Scrapping using network libraries in python
  • Data extration from Zomato/Ola/Amazon or other such big online platform

Project 10:- Smart Weather App using ML & Python

2 Hours
Day 21

Natural Language Processing

  • Concepts of Natural Language processing
  • NLP libraries in Python
  • Working with NLTK
  • Words Extrations from the text
  • Sentiment Analysis concepts

Project 11:- Smart Talking System using ML

2 Hours
Day 22

Keras Library and Its Implementation

  • Understanding wide range of Keras library
  • Keras and its various structures for images
  • Backend tensorflow mechanism for patern recognitions
  • Deep learning models and their formations
  • Deep learning models use case with ML for Expression Recognition

Project 12:- Facial Expression Recognition System

2 Hours
Day 23

Deep Learning Concepts

  • Understanding Deep Learning
  • Various Concepts of Deep Learning
  • How does these model work?
  • How to prepare your own Models?
  • Various problems to work with Deep Learning

Project 13:- Preparation of Self Deep Learning Models using Custom datasets

2 Hours
Day 24

Data Science with R

  • Basics of R Programming
  • Data Types and its usecases in data science
  • Functions and modules in R for data science
  • Data Plotting using R Language
  • Data Visualisation and Analysis with R
2 Hours
Day 25 & 26

Project Completion

Query Session

2 Hours
Machine Learning with Data Science 45 Days
Course:Machine Learning
Certification By:TechieNest, An ISO 9001:2008 Certified Company
Study Material:Book free to each participant (Soft Copy)
FeeINR 15,000/- + Taxes
Duration50 Days/ 100 Hours

Course Details


Machine Learning
DAYTOPICDURATION
Day 27

Seaborn Library for Graphical Data Visualisation

  • Plotting Data
  • Various Graphs and analysis
  • Various Tactics and methods of graphs plotting
  • Real Data sets visualisation
  • Seaborn verses Matplotlib
  • Seaborn Verses Tableau

Project 14:- Data Analyics Software Development

2 Hours
Day 28

Advance Data Science and Analytics

  • Advance tools and libraries for Data Science
  • Data Mining, Custom Data Formation
  • Data Scraping and Data pattern recognition
  • Data Storage and Visualisation
  • SQL databases and connections through python
  • GITHUB exposure

Software installations

2 Hours
Day 29Image Data Analysis, Extraction and Manipulations

  • Working with Python and Opencv
  • Image Rescaling, Binarisation, Noise Removal
  • Image Deskewing
  • Transformations in images
  • Analysis on various types of images

Project 15:- Image content Deep Analysis

4 Hours
Day 30

Machine Learning with Data Science & Advance Computer Vision

  • Text analysis in the images
  • Words and characters ROI generation
  • Various formulations for image data training sets
  • Machine Learning with extracted image data

Project 16:- Vehicle Number plate Detection

Project 17:- Image to Text Conversions in Offline mode

4 Hours
Day 31

Deep Learning

  • What’s Deep Learning and differ from Machine Learning?
  • Deep neural Network
  • Machine training and models
  • Transfer Learning
  • Ensemble Learning
4 Hours
Day 32 & 33

Artificial Neural Network (ANN)

  • Deep learning using ANN
  • ANN fundamentals and its network parameters
  • Feed Forward mechanism
  • Back Propagation in Neural networks
  • Gradient Descent in Neural Networks
  • Mathematical Approach and Analysis of ANN
  • ANN Implementation on various data sets
4 Hours
Day 34 & 35

Recurrent Neural Network (RNN)

  • Basics of RNN
  • Problem solutions using RNN
  • Deep learning using RNN
  • Mathematics behind RNN
  • RNN Implementation using Python
  • Types of RNN
4 Hours
Day 36

Deep learning Models Preparation

  • Various Architecture for DL models
  • Custom Datasets formations
  • Data preprocessing
  • Data Augmentation
  • Informative Data Extractions
  • Preparing your own DL Models for Machine Learning
2 Hours
Day 37 & 38

Deep Learning using Keras

  • Deep Dive with Keras
  • Implementation of custom keras layers, loss functions
  • Custom data generators
  • Keras implementation using python on images
  • DL models preparation using Keras
2 Hours
Day 39

Project 18 : Stock price Prediction

Project 19 : Personalized assistant

Project Completion

Query Session

2 Hours
Machine Learning with Data Science 60 Days
Course:Machine Learning
Certification By:TechieNest, An ISO 9001:2008 Certified Company
Study Material:Book free to each participant (Soft Copy)
FeeINR 15,000/- + Taxes
Duration50 Days/ 100 Hours

Course Details


Machine Learning
DAYTOPICDURATION
Day 40

Tensorflow 2.0

  • Eager Tensor
  • Tensorflow datasets and TFRecords
  • Learning rate scheduling
  • Multi GPU training
2 Hours
Day 41

Caffe Deep Learning Framework

  • Fundamentals of the library
  • Caffe implementation using Python

Project 20 : Image Colorization

2 Hours
Day 42Pytorch Deep Learning Framework

  • Pytorch a Python Library
  • Fundamentals of the library
  • Pytorch Vs Caffe Vs Tensorflow
  • DL using Pytorch

Project 21 : Artistic style transfer

2 Hours
Day 43 & 44

Natural Language Processing (NLP) with Deep Learning

  • Word and sentence extraxtions
  • Meaningful clusters formations
  • NLP using Python

Project 22: Toxic comment classification

4 Hours
Day 45 & 46

Reinforcement Learning

  • Q-Learning
  • Implentation of Q-Learning with Python.
  • Priortized Experince Replay.
  • Dual Deep Q-Learning.

Project 23: CartPole Self Balancing agent

4 Hours
Day 47 & 48

Project 24:- Advance Face Recogntion System

Project 25:- Self Learning System

Project 26:- Jarvis 2.0

4 Hours
Day 49 & 50

Project 27:- ML for Emotions Detection

Project 28:- ML in AR & VR

4 Hours
Day 51 & 52

Project 29:- Advance Recommendation System using ML

Project completion & query session

4 Hours

HIGHLIGHTS OF THE COURSE

For 30 Days Course(4 Weeks)

  • Basic ML Algorithms(Decision Tree, Naïve Bayes, SVM, Regression,etc)
  • Data science modules(Numpy, Pandas, Matplotlib)
  • Basics of Image Processing, Speech Recognition
  • Neural Network and NLP
  • Tensorflow with Keras module
  • Data science with R

13 + Projects Covered

For next 15 Days Course(5-6th Week)

  • Advanced Data visualisation using Seaborn
  • Advanced Image Processing
  • ANN,RNN
  • Train our model with transfer learning

6+ Projects Covered

For next 15 Days Course(7-8th Week)

  • Basic of Tensorflow 2.0
  • Deep Learning Framework(Caffe,Pytorch)
  • Advanced NLP
  • Reinforcement Learning

10+ Projects Covered

* Apart from the course content, we are organizing special session on resume writing, soft skills, personality development and mock interview sessions for our techies.

Step 1

Register online for any desired course, duration & location of your training course & obtain a Registration-ID. Registration-ID is a Unique Registration Number which is generated by our system after successful registration for training A student can have multiple IDs for multiple courses & batches. It is displayed while successful registration and it is also mailed to you immediately after registration by our server. if you don’t find it in your mail then, please check your SPAM folder or junk folder of your mail ID.

Step 2

Please deposit your Course fee to any one of our payment gateway/ Bank Account/ paytm.

Payment Gateway link: PayUmoney gateway

Bank Account Details

TechieNest Pvt Ltd

Indusind Bank Limited

Malviya Nagar
Jaipur ( Rajasthan)
Account No : 201000689491
IFSC: INDB0000592

Paytm Number9251494002

Step 3

Update us regarding your fee payment by sending picture/scan copy of bank receipt to: training@techienest.in and you will receive a confirmation mail on your mail id.

When someone says yes you can do it….it means you can achieve it and when you decide to take an action we come with the surprising offers:

1. Group Discount:

Offer code: TNGD-5
Offer code: TNGD-10
Offer code: TNGD-15

  • If a group size is of: 5 -10 then 5% discount on training
  • 10-20 then 10% discount on training
  • 20 and above then 15% discount on training
2. Referral Offer:

Offer code: TNR3
Offer code: TNR5

  • 3% additional discount to the person who is referring
  • 5% additional discount to the one who is being referred
3. For Former students up to 15% off:

Offer code: TNFS15

  • There will be 15% discount on students who already did training
4. Previous Workshop attended students 5% off:

Offer code: TNPW5

5. 5% additional Discount for Campus Ambassador:

Offer code: TNA5

Certification

All participants will get Certificate from TechieNest Pvt. Ltd. in association with Aavriti’18 IIT Bombay

Why TechieNest

  • Vast experience of having conducted Big Outreach Workshop collaborating with over 300+ colleges in all over India including IIT Bombay, IIT Hyderabad, IIT Bhubaneswar, IIT Jodhpur, IIT Mandi, NIT Raipur, MNIT Jaipur, MANIT Bhopal, NIT Jalandhar, NIT Patna, NIT Srinagar, IIIT Kalyani, BITS Pilani and likewise.
  • Trained more than 20,000 students in the field of EMBEDDED SYSTEMS & ROBOTICS, MATLAB & Machine Vision, Internet of Things, PLC_SCADA, PYTHON, C/C++, Andriod, VLSI & VHDL, JAVA and such top notch courses.
  • Our trainers are efficient in Raspberry pi, Arduino, PLCs, etc. which forms essential hardware in Electronic Industries nowadays.
  • Outreach workshop partner of Sanchaar-Wissenaire’18, IIT Bhubaneswar, 2017-18
  • Zonal workshop partner of Techkriti’18 IIT Kanpur, 2017-2018
  • Outreach workshop partner of Techfest’15 IIT Bombay & Techfest’16 IIT Bombay
  • Zonal workshop partner of Techkriti’17 IIT Kanpur, 2016-2017
  • Outreach workshop & Training partner of nVision’17 IIT Hyderabad, 2016-17
  • Outreach workshop partner of Ignus’17 IIT Jodhpur, 2016-17
  • AIRC’18 (All India Robotics Championship) in association with Techkriti’18 IIT Kanpur.
  • AIRC’17 (All India Robotics Championship) in association with nVision’17 IIT Hyderabad, 2016-17
  • Offering Project Based Training, Projects on Demand, Corporate Projects, Commercial Projects, and Consultancy in Engineering Projects.
    Dedicated 24×7 R&D lab.
  • Trained over 50+ international students in TechieNest Technology Transfer Program 2014-15.
  • TechieNest has Research Engineers having excellent research aptitude, teaching pedagogy who illustrates their finding through practical demos during workshop/training.
  • Manufacturer of Electronic products delivering the same across the country.
Course
Machine Learning with Data Science 30 Days
Course:Machine Learning
Certification By:TechieNest, An ISO 9001:2008 Certified Company
Study Material:Book free to each participant (Soft Copy)
FeeINR 9,500/- + Taxes
Duration1 Months/ 50 Hours

Course Details


Machine Learning
DAYTOPICDURATION
Day 1

Basics of Python Programming

  • Environment Setup
  1. Installing Python
  2. Setting up path
  • Basic Syntax
  • Data Types
  1. Numbers
  2. String
  3. List
  4. Tuple
  5. Dictionary
  • Decision Making(Loops, Conditional Statements)
  • Functions
  • Basic Libraries
  1. Math
  2. Date & Time
  3. Random
  4. Request
4 Hours
Day 2

Machine Learning

  • Introduction to machine learning
  • Understanding the need
  • Understanding Big data and machine learning
  • Running machine learning under Linux platform
  • Why Linux is important for machine learning with respect to future
  • Basic Introduction of Python syntax and programming logic
  • Deep dive with Supervised, Unsupervised and Reinforcement learning
  • Algo discussion with use case

Advance Python programming and its use case

  • Basic of python and why python for machine learning
  • Installation of software and libraries on different OS.
  • Revising python concepts
  • Advance python programming
  • Hands-on with Python standard libraries
  • GITHUB exposure
4 Hours
Day 3Data Science Libraries

  • Understanding & use of Various Open source libraries
  • Importing various modules with different methods
  • File handling with Python
  • Working with Numpy
  • Data types and its various Numerical operations
  • Exploring various use cases of Numpy
  • Hands-on with huge data using Numpy
4 Hours
Day 4

Pandas & Matplotlib Libraries

  • Fundamentals of pandas
  • Data frames and their operations
  • .csv .xml and various files data import
  • Data extraction, update and export
  • Fundamentals of Matplotlib
  • Various 2D & 3D graphs
  • Data visualisation in various types of graphs

Project – Data Analytics using Python

4 Hours
Day 5

Computer Vision & OpenCV Library

  • Fundamentals of Computer Vision.
  • Image Processing using Python.
  • OpenCV library for various data operations.
  • Working with live data.
  • Computer Vision for various fields like AR, VR, ML etc.
  • Morphological operations and Image Filtering & ROI Extractions.
  • Color Marker Detection.
  • Face Detection
4 Hours
Day 7

Naive Bayes

  • Probability of Various Events
  • Bayes Theorem
  • Practice lab with Decision Tree algo and number of examples
  • Training data with python using Naive Bayes/li>
  • Deep dive with UCI
  • Lab session for loading data from different apis
  • Detecting data from numpy and converting for training and testing data
  • Exercise with ML and others framework
2 Hours
Day 8

ML Continued with Real Data set

  • Introduction to iris datasets
  • Understanding iris datasets
  • Modifying and loading with pandas
  • Separating data with numpy
  • Training classifier
  • Algo data process view
  • Decision Tree & Naive Bayes understanding & Results Comparisons
2 Hours
Day 9

K Nearest Neighbours – KNN algo)

  • Understanding the Mathematics and working of KNN
  • Implementing KNN by your Own
  • Apply your own designed KNN on real datasets
  • Comparing Designed KNN Results with Sklearn implementations
  • Applications of KNN
2 Hours
Day 10

Regression (Linear Regression)

  • Understanding functioning of the Algo and Its Mathematics
  • Implementing algo and applying datasets to it
  • Difference between Regression and Classification
  • Working with the real datasets
  • Stock exchange/GDP/Growth of the company analysis
  • Writing various codes upon various datasets
2 Hours
Day 11

SVM (Supprt Vector Machine)

  • Support Vector Classifier and Regression
  • Understanding functioning of the Algo and Its Mathematics
  • Understanding Hyperplanes and its various internal parameters
  • Implementing algo and applying datasets to it
  • Difference between Regression and Classification
  • Working with the real datasets
  • Writing various codes upon various datasets
2 Hours
Day 12

Clustering (K-Means)

  • Unsupervised Learning
  • Features and data vectors visualisation
  • Various steps of algo implementation
  • Understanding of Clusters and various types of Clustering
  • Applying K-Means on datasets and their practical usecases
  • Applications of Clustering and the algorithm
2 Hours
Day 13

Neural Network (NN)

  • What’s Neural Network?
  • Various Structures of NN
  • Understanding Fundamentals and Various parameters of NN
  • ANN,CNN and RNN
  • Deep Dive with the Implementaion of NN on various datasets
  • Applying CNN on Images
  • Applications and its complexities over other algorithms

Project 2:- Smart Machine Learning System

2 Hours
Day 14

Objects Detections

  • Image processing and it’s various features for detection
  • Haar Classifier and its fuctioning behind
  • Cascading of features in Algorithm
  • Implementation of Haar Classifier on different image datasets
  • Realtime Object Detection

Project 3:- Object Detection System

2 Hours
Day 15

Tensorflow

  • Fundamentals of tensorflow
  • What’s tensor and its flow graphs
  • Datatypes and Data Optimizers
  • Understanding Tensorflow from basics
  • Implementing usecase using tensorflow
  • Working on Realtime problem with Tensorflow and writing code for that
2 Hours
Day 16

Obejcts Recognitions

  • Understanding of Features of Objects for Recognitions
  • Working with Face Recognition Library
  • Recognition encodings
  • Various matching techniques for Recognition
  • Working on improving Efficiency of the code

Face Recognition System

Biometric Advance Attendance System

2 Hours
Day 17

Projects Continued

Project 6:- Smart Music App using ML & Python

Project 7:- Building Security System

2 Hours
Day 18

API Integration with Python

  • What is an API?
  • What is Cloud & its Connections with Python?
  • Google Python Libraries
  • Speech Recognition
  • Text to Speech Conversion
  • Speech Recognition Exceptions
  • Various API’s Integration for ML

Project 8 :- Design & Development of your Personal Assistant

2 Hours
Day 19

ML over Cloud

  • Various cloud platforms for ML
  • Open Source Cloud for Features engineering
  • Various features for a person analysis
  • Registration and deletion of data over cloud
  • Recognising images over cloud

Project 9:- Gender & Expressions recognition system over cloud

2 Hours
Day 20

Weather & Other API’s

  • Various weather API’s
  • Data extration from the raw weather information
  • Other API’s for data extraction from Web
  • Web Scrapping using network libraries in python
  • Data extration from Zomato/Ola/Amazon or other such big online platform

Project 10:- Smart Weather App using ML & Python

2 Hours
Day 21

Natural Language Processing

  • Concepts of Natural Language processing
  • NLP libraries in Python
  • Working with NLTK
  • Words Extrations from the text
  • Sentiment Analysis concepts

Project 11:- Smart Talking System using ML

2 Hours
Day 22

Keras Library and Its Implementation

  • Understanding wide range of Keras library
  • Keras and its various structures for images
  • Backend tensorflow mechanism for patern recognitions
  • Deep learning models and their formations
  • Deep learning models use case with ML for Expression Recognition

Project 12:- Facial Expression Recognition System

2 Hours
Day 23

Deep Learning Concepts

  • Understanding Deep Learning
  • Various Concepts of Deep Learning
  • How does these model work?
  • How to prepare your own Models?
  • Various problems to work with Deep Learning

Project 13:- Preparation of Self Deep Learning Models using Custom datasets

2 Hours
Day 24

Data Science with R

  • Basics of R Programming
  • Data Types and its usecases in data science
  • Functions and modules in R for data science
  • Data Plotting using R Language
  • Data Visualisation and Analysis with R
2 Hours
Day 25 & 26

Project Completion

Query Session

2 Hours
Machine Learning with Data Science 45 Days
Course:Machine Learning
Certification By:TechieNest, An ISO 9001:2008 Certified Company
Study Material:Book free to each participant (Soft Copy)
FeeINR 15,000/- + Taxes
Duration50 Days/ 100 Hours

Course Details


Machine Learning
DAYTOPICDURATION
Day 27

Seaborn Library for Graphical Data Visualisation

  • Plotting Data
  • Various Graphs and analysis
  • Various Tactics and methods of graphs plotting
  • Real Data sets visualisation
  • Seaborn verses Matplotlib
  • Seaborn Verses Tableau

Project 14:- Data Analyics Software Development

2 Hours
Day 28

Advance Data Science and Analytics

  • Advance tools and libraries for Data Science
  • Data Mining, Custom Data Formation
  • Data Scraping and Data pattern recognition
  • Data Storage and Visualisation
  • SQL databases and connections through python
  • GITHUB exposure

Software installations

2 Hours
Day 29Image Data Analysis, Extraction and Manipulations

  • Working with Python and Opencv
  • Image Rescaling, Binarisation, Noise Removal
  • Image Deskewing
  • Transformations in images
  • Analysis on various types of images

Project 15:- Image content Deep Analysis

4 Hours
Day 30

Machine Learning with Data Science & Advance Computer Vision

  • Text analysis in the images
  • Words and characters ROI generation
  • Various formulations for image data training sets
  • Machine Learning with extracted image data

Project 16:- Vehicle Number plate Detection

Project 17:- Image to Text Conversions in Offline mode

4 Hours
Day 31

Deep Learning

  • What’s Deep Learning and differ from Machine Learning?
  • Deep neural Network
  • Machine training and models
  • Transfer Learning
  • Ensemble Learning
4 Hours
Day 32 & 33

Artificial Neural Network (ANN)

  • Deep learning using ANN
  • ANN fundamentals and its network parameters
  • Feed Forward mechanism
  • Back Propagation in Neural networks
  • Gradient Descent in Neural Networks
  • Mathematical Approach and Analysis of ANN
  • ANN Implementation on various data sets
4 Hours
Day 34 & 35

Recurrent Neural Network (RNN)

  • Basics of RNN
  • Problem solutions using RNN
  • Deep learning using RNN
  • Mathematics behind RNN
  • RNN Implementation using Python
  • Types of RNN
4 Hours
Day 36

Deep learning Models Preparation

  • Various Architecture for DL models
  • Custom Datasets formations
  • Data preprocessing
  • Data Augmentation
  • Informative Data Extractions
  • Preparing your own DL Models for Machine Learning
2 Hours
Day 37 & 38

Deep Learning using Keras

  • Deep Dive with Keras
  • Implementation of custom keras layers, loss functions
  • Custom data generators
  • Keras implementation using python on images
  • DL models preparation using Keras
2 Hours
Day 39

Project 18 : Stock price Prediction

Project 19 : Personalized assistant

Project Completion

Query Session

2 Hours
Machine Learning with Data Science 60 Days
Course:Machine Learning
Certification By:TechieNest, An ISO 9001:2008 Certified Company
Study Material:Book free to each participant (Soft Copy)
FeeINR 15,000/- + Taxes
Duration50 Days/ 100 Hours

Course Details


Machine Learning
DAYTOPICDURATION
Day 40

Tensorflow 2.0

  • Eager Tensor
  • Tensorflow datasets and TFRecords
  • Learning rate scheduling
  • Multi GPU training
2 Hours
Day 41

Caffe Deep Learning Framework

  • Fundamentals of the library
  • Caffe implementation using Python

Project 20 : Image Colorization

2 Hours
Day 42Pytorch Deep Learning Framework

  • Pytorch a Python Library
  • Fundamentals of the library
  • Pytorch Vs Caffe Vs Tensorflow
  • DL using Pytorch

Project 21 : Artistic style transfer

2 Hours
Day 43 & 44

Natural Language Processing (NLP) with Deep Learning

  • Word and sentence extraxtions
  • Meaningful clusters formations
  • NLP using Python

Project 22: Toxic comment classification

4 Hours
Day 45 & 46

Reinforcement Learning

  • Q-Learning
  • Implentation of Q-Learning with Python.
  • Priortized Experince Replay.
  • Dual Deep Q-Learning.

Project 23: CartPole Self Balancing agent

4 Hours
Day 47 & 48

Project 24:- Advance Face Recogntion System

Project 25:- Self Learning System

Project 26:- Jarvis 2.0

4 Hours
Day 49 & 50

Project 27:- ML for Emotions Detection

Project 28:- ML in AR & VR

4 Hours
Day 51 & 52

Project 29:- Advance Recommendation System using ML

Project completion & query session

4 Hours
Highlights

HIGHLIGHTS OF THE COURSE

For 30 Days Course(4 Weeks)

  • Basic ML Algorithms(Decision Tree, Naïve Bayes, SVM, Regression,etc)
  • Data science modules(Numpy, Pandas, Matplotlib)
  • Basics of Image Processing, Speech Recognition
  • Neural Network and NLP
  • Tensorflow with Keras module
  • Data science with R

13 + Projects Covered

For next 15 Days Course(5-6th Week)

  • Advanced Data visualisation using Seaborn
  • Advanced Image Processing
  • ANN,RNN
  • Train our model with transfer learning

6+ Projects Covered

For next 15 Days Course(7-8th Week)

  • Basic of Tensorflow 2.0
  • Deep Learning Framework(Caffe,Pytorch)
  • Advanced NLP
  • Reinforcement Learning

10+ Projects Covered

* Apart from the course content, we are organizing special session on resume writing, soft skills, personality development and mock interview sessions for our techies.

How to Enroll

Step 1

Register online for any desired course, duration & location of your training course & obtain a Registration-ID. Registration-ID is a Unique Registration Number which is generated by our system after successful registration for training A student can have multiple IDs for multiple courses & batches. It is displayed while successful registration and it is also mailed to you immediately after registration by our server. if you don’t find it in your mail then, please check your SPAM folder or junk folder of your mail ID.

Step 2

Please deposit your Course fee to any one of our payment gateway/ Bank Account/ paytm.

Payment Gateway link: PayUmoney gateway

Bank Account Details

TechieNest Pvt Ltd

Indusind Bank Limited

Malviya Nagar
Jaipur ( Rajasthan)
Account No : 201000689491
IFSC: INDB0000592

Paytm Number9251494002

Step 3

Update us regarding your fee payment by sending picture/scan copy of bank receipt to: training@techienest.in and you will receive a confirmation mail on your mail id.

Fee & Discount

When someone says yes you can do it….it means you can achieve it and when you decide to take an action we come with the surprising offers:

1. Group Discount:

Offer code: TNGD-5
Offer code: TNGD-10
Offer code: TNGD-15

  • If a group size is of: 5 -10 then 5% discount on training
  • 10-20 then 10% discount on training
  • 20 and above then 15% discount on training
2. Referral Offer:

Offer code: TNR3
Offer code: TNR5

  • 3% additional discount to the person who is referring
  • 5% additional discount to the one who is being referred
3. For Former students up to 15% off:

Offer code: TNFS15

  • There will be 15% discount on students who already did training
4. Previous Workshop attended students 5% off:

Offer code: TNPW5

5. 5% additional Discount for Campus Ambassador:

Offer code: TNA5

Certification

Certification

All participants will get Certificate from TechieNest Pvt. Ltd. in association with Aavriti’18 IIT Bombay

Why TechieNest

  • Vast experience of having conducted Big Outreach Workshop collaborating with over 300+ colleges in all over India including IIT Bombay, IIT Hyderabad, IIT Bhubaneswar, IIT Jodhpur, IIT Mandi, NIT Raipur, MNIT Jaipur, MANIT Bhopal, NIT Jalandhar, NIT Patna, NIT Srinagar, IIIT Kalyani, BITS Pilani and likewise.
  • Trained more than 20,000 students in the field of EMBEDDED SYSTEMS & ROBOTICS, MATLAB & Machine Vision, Internet of Things, PLC_SCADA, PYTHON, C/C++, Andriod, VLSI & VHDL, JAVA and such top notch courses.
  • Our trainers are efficient in Raspberry pi, Arduino, PLCs, etc. which forms essential hardware in Electronic Industries nowadays.
  • Outreach workshop partner of Sanchaar-Wissenaire’18, IIT Bhubaneswar, 2017-18
  • Zonal workshop partner of Techkriti’18 IIT Kanpur, 2017-2018
  • Outreach workshop partner of Techfest’15 IIT Bombay & Techfest’16 IIT Bombay
  • Zonal workshop partner of Techkriti’17 IIT Kanpur, 2016-2017
  • Outreach workshop & Training partner of nVision’17 IIT Hyderabad, 2016-17
  • Outreach workshop partner of Ignus’17 IIT Jodhpur, 2016-17
  • AIRC’18 (All India Robotics Championship) in association with Techkriti’18 IIT Kanpur.
  • AIRC’17 (All India Robotics Championship) in association with nVision’17 IIT Hyderabad, 2016-17
  • Offering Project Based Training, Projects on Demand, Corporate Projects, Commercial Projects, and Consultancy in Engineering Projects.
    Dedicated 24×7 R&D lab.
  • Trained over 50+ international students in TechieNest Technology Transfer Program 2014-15.
  • TechieNest has Research Engineers having excellent research aptitude, teaching pedagogy who illustrates their finding through practical demos during workshop/training.
  • Manufacturer of Electronic products delivering the same across the country.
Center

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